Application of Carbon Nanotubes in Nanomedicine

Carbon Nanotubes (CNTs) have become a technological field with great potential since they can be applied in almost every aspect of modern life. One of the sectors where CNTs are expected to play a vital role is the field of medical science. This chapter focuses on the latest developments in applications of CNTs for nanomedicine. A brief history of CNTs and a general introduction to the field are presented. Then, the preparation of CNTs that makes them ideal for use in medical applications is highlighted. Examples of common applications, including cell penetration, drug delivery, and gene delivery and imaging are given. Finally, the toxicity of carbon nanotubes is discussed.

New Diagnostic and Monitoring Method for Osteoporosis

Osteoporosis is chronic disease affecting most postmenopausal females and 30% of males with biological, behavioral and financial consequences. A non invasive method to assess bone structural integrity is presented, based on in-vitro or in-vivo measurement of bone dynamic characteristics (Modal Damping Factor) by applying vibration excitation in the range of acoustic frequencies, in the form of an acoustic sweep signal. This method has been applied on metallic structures and composites, including bones, and is supported by analytical and arithmetic tool based on model's theory. Experimental MDF results are compared to results acquired with conventional methods for bone quality assessment and show impressive correlations between damping factor and indices of bone quality in an advantageous manner. Evaluation of these research findings strengthens the potential of the proposed method to consist a valuable assessment tool for diagnosis and monitoring of bone integrity, in metabolic bone diseases, especially osteoporosis.

An Approach for Automatic Detection and Grading of Macular Edema

Prolonged Diabetes causes massive destruction to the retina, known as Diabetic Retinopathy (DR) leading to blindness. The blindness due to DR may consequence from several factors such as Blood vessel (BV) leakage, new BV formation on retina. The effects become more threatening when abnormalities involves the macular region. Here automatic analysis of fundus images becomes important. This system checks for any abnormality and help ophthalmologists in decision making and to analyze more number of cases. The main objective of this chapter is to explore image processing tools for automatic detection and grading macular edema in fundus images.

Neuroimaging Approaches for Elderly Studies

Advances in the field of neuroimaging have allowed for the examination of the effects of age-related changes on cognitive capacity in elderly populations. Structural techniques are now routinely used to report cortical atrophic rates in aging and particularly within the context of the Alzheimer's disease, and may be integrated with functional techniques which examine the functional characteristics of the cortex at rest and during the performance of a task. Despite advancing age cognitive function remains highly plastic, allowing for interventions that aim to maintain or even remediate its capacity and the mechanisms by which structure and function are altered among seniors. Overall, information on the integrity of the cerebral structure and function aid in the early detection and treatment of the Alzheimer's disease as well as the evaluation and track of the disease's progression. In this chapter, neuroimaging methods are presented along with findings that are particularly relevant for the study of neuroplasticity in the aging brain.

Evaluations on the Applicability of Generic and Modular Image Processing Chains for Quantitative 3D Data Analysis in Clinical Research and Radiographer Training

The introduction of digital imaging and diagnostics facilities has fundamentally changed radiology. Nevertheless, theory of digital image processing and analysis as well as their practical application are still only a subsidiary part in nowadays radiology technician curricula. This work focuses on the evaluation, to what extent the authors' simplified and standardized process model for applying image processing modules in generic domains is suited for radiographer students and medical staff, lacking deeper theoretical knowledge compared to physicians and imaging experts. The semi-automated image processing workflow thereby comprises region growing, live-wire segmentation and filtering steps, all available from MeVisLab prototyping framework. It is shown that the proposed imaging chain is highly applicable for analysis and facilitating medical diagnostics of arbitrary anatomical structures from tomographic data. After compact practical instruction, radiographer students are versed to achieve complex 3D analysis perfectly suited for quantitative analysis in clinical research typically only achievable by use of specialized software.

Cluster Based Medical Image Registration Using Optimized Neural Network

Registration of medical images like CT-MR, MR-MR etc. are challenging area for researchers. This chapter introduces a new cluster based registration technique with help of the supervised optimized neural network. Features are extracted from different cluster of an image obtained from clustering algorithms. To overcome the drawback regarding convergence rate of neural network, an optimized neural network is proposed in this chapter. The weights are optimized to increase the convergence rate as well as to avoid stuck in local minima. Different clustering algorithms are explored to minimize the clustering error of an image and extract features from suitable one. The supervised learning method applied to train the neural network. During this training process an optimization algorithm named Genetic Algorithm (GA) is used to update the weights of a neural network. To demonstrate the effectiveness of the proposed method, investigation is carried out on MR T1, T2 data sets. The proposed method shows convincing results in comparison with other existing techniques.

Classification of Brain MR Images Using Corpus Callosum Shape Measurements

The corpus callosum is the largest white matter structure in the brain, which connects the two cerebral hemispheres and facilitates the inter-hemispheric communication. Abnormal anatomy of corpus callosum has been revealed for various brain related diseases. Being an important biomarker, Magnetic Resonance Imaging of the brain followed by corpus callosum segmentation and feature extraction has found to be important for the diagnosis of many neurological diseases. This paper focuses on classification of T1-weighted mid-sagittal MR images of brain for dementia patients. The corpus callosum is segmented using K-means clustering algorithm and corresponding shape based measurements are used as features. Based on these shape based measurements, a back-propagation neural network is trained separately for male and female dataset. The input data consists of 54 female and 31 male patients. This paper reports classification accuracy up to 92% for female patients and 94% for male patients using neural network classifier.

Analysis and Comparison of Developed 2D Medical Image Database Design using Registration Scheme, Retrieval Scheme, and Bag-of-Visual-Words

There is certain to be a significant increase in the use of registration, retrieval and registration based retrieval of medical images in healthcare. But there is a related major concern that these initiatives are narrowly policy or technology led rather than evidence based or case based reasoning. Or else, either they will be based on old paradigm structures without considering the new paradigms that they create. It is planned to look at the image registration and retrieval from a unified perspective based on their performance for clinical diagnosis and treatment. It is concluded with an experiment carried out on histopathology and five types of anatomic (radiology) images. The results showed that this approach works better with more images from the Bag-Of-Visual words and Affine with B-Spline registration based retrieval for modality and histopathological images. Bag-of-Visual Words is more suitable for histopathological images than registration based retrieval techniques.

Study of Hardness and Corrosion Resistance of Electroless Ni-P-TiO2 Composite Coatings

Electroless nickel coatings are widely popular in various industrial sectors due to their excellent tribological properties. The present study considers optimization of coating parameters along with annealing temperature to improve microhardness and corrosion resistance of Ni-P-TiO2 composite coatings. Grey relational analysis is used to find out the optimal combination of coating parameters. From the analysis, it is confirmed that annealing temperature of the coating has the most significant effect and amount of titanium particles in the coating has some significant effect on corrosion properties of the coating. The same trend is observed in case of combined study of corrosion behavior and microhardness. The surface morphology, phase transformation and the chemical composition are examined using scanning electron microscopy, X-ray diffraction analysis and energy dispersive analysis respectively. The Ni-P-TiO2 composite coating revealed nodular structure with almost uniform distribution of titanium particles and it turns in to crystalline structure after heat treatment.

Comprehensive Survey on Metal Artifact Reduction Methods in Computed Tomography Images

Over the past few years, medical imaging technology has significantly advanced. Today, medical imaging modalities have been designed with state-of-the-art technology to provide much better in-depth resolution, reduced artifacts, and improved contrast –to – noise ratio. However in many practical situations complete projection data is not acquired leading to incomplete data problem. When the data is incomplete, tomograms may blur, resolution degrades, noise increases and forms artifacts which is the most important factor in degrading the tomography image quality and eventually hinders diagnostic accuracy. Efficient strategies to address this problem and to improve the diagnostic acceptability of CT images are thus invaluable. This review work, presents comprehensive survey of techniques for minimization of streaking artifact due to metallic implant in CT images. Problematic issues and outlook for the future research are discussed too. The major goal of the paper is to provide a comprehensive reference source for the researchers involved in metal artifact reduction methods.